A scale-space primer for exploring and quantifying complex landscapes

Over the last two decades, the scale-space community has developed into a reputable field in computer vision, yet its nontrivial mathematics (i.e. group invariance, differential geometry and tensor analysis) limit its adoption by a larger body of researchers and scientists, whose interests in multiscale analysis range from biomedical imaging to landscape ecology. In an effort to disseminate the ideas of this community to a wider audience we present this non-mathematical primer, which introduces the theory, methods, and utility of scale-space for exploring and quantifying multi-scale landscape patterns within the context of Complex Systems theory. In addition, we suggest that Scale-Space theory, combined with remote sensing imagery and blob-feature detection techniques, satisfy many of the requirements of an idealized multiscale framework for landscape analysis.